SoroushVahidi/scidocs huggingface dataset:
Leakage Current Analytical Model and Application in Single-Phase Transformerless Photovoltaic Grid-Connected Inverter
(2010)

Due to the characteristics of low cost and high efficiency, the transformerless photovoltaic (PV) grid-connected inverters have been popularized in the application of solar electric generation system in residential market. Unfortunately, the leakage current through the stray capacitors between the PV array and the ground is harmful. This paper focuses on the leakage current suppressing method, in which all common-mode paths are considered. First, the common-mode analytical model at switching frequency is developed, and the rules of eliminating switching frequency common-mode source are summarized based on this model. The existing full-bridge- and half-bridge-type converters have been analyzed by using the developed model and rules, and then, a new full-bridge-type converter structure and a compensation strategy for half-bridge-type inverter have been presented finally.
Cyber-Physical System Security of a Power Grid : State-ofthe-Art
(2016)

As part of the smart grid development, more and more technologies are developed and deployed on the power grid to enhance the system reliability. A primary purpose of the smart grid is to significantly increase the capability of computer-based remote control and automation. As a result, the level of connectivity has become much higher, and cyber security also becomes a potential threat to the cyber-physical systems (CPSs). In this paper, a survey of the state-of-the-art is conducted on the cyber security of the power grid concerning issues of: (1) the structure of CPSs in a smart grid; (2) cyber vulnerability assessment; (3) cyber protection systems; and (4) testbeds of a CPS. At Washington State University (WSU), the Smart City Testbed (SCT) has been developed to provide a platform to test, analyze and validate defense mechanisms against potential cyber intrusions. A test case is provided in this paper to demonstrate how a testbed helps the study of cyber security and the anomaly detection system (ADS) for substations.
The paradox of received social support: the importance of responsiveness.
(2009)

Although the perception of available support is associated with positive outcomes, the receipt of actual support from close others is often associated with negative outcomes. In fact, support that is "invisible" (not perceived by the support recipient) is associated with better outcomes than "visible" support. To investigate this paradox, we proposed that received support (both visible and invisible) would be beneficial when it was responsive to the recipient's needs. Sixty-seven cohabiting couples participated in a daily-experience study in which they reported on the support they provided and received each day. Results indicated that both visible and invisible support were beneficial (i.e., associated with less sadness and anxiety and with greater relationship quality) only when the support was responsive. These findings suggest that the nature of support is an important determinant of when received support will be beneficial.
BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs
(1999)

SUMMARY
BAliBASE is a database of manually refined multiple sequence alignments categorized by core blocks of conservation sequence length, similarity, and the presence of insertions and N/C-terminal extensions.


AVAILABILITY
From http://www-igbmc. u-strasbg.fr/BioInfo/BAliBASE/index.html
Pneumatic muscle actuators within robotic and mechatronic systems
(2015)

Pneumatic artificial muscles (PAMs) as soft, lightweight and compliant actuators have great potential in applications for the actuations of new types of robots and manipulators. The favourable characteristics of fluidic muscles, such as high power-to-weight ratio and safe interaction with humans are also very suitable during the process of musculoskeletally rehabilitating patients and are often used in making artificial orthoses. This technology, despite the problems of control relatng to nonlinear phenomena, may also have wide future applications within industrial and mechatronic systems. This paper presents several experimental systems actuated by PAMs, which have been designed as test models within the fields of mobile robots, mechatronics, fluid power systems and the feedback control education of mechanical engineering students. This paper first presents the design and construction of a four legged walking robot actuated by pneumatic muscles. The robot has a fully autonomous system with a wireless application platform and can be controlled using a cell phone. Then the paper describes the design and construction of the prototype of an actively-powered ankle foot orthosis. This orthosis device actuated by a single PAM is able to provide the appropriate functions required during the rehabilitations of patients and the loss of mobility. Then the paper focuses on the design and control of a ball and beam system with an antagonistic muscle pair for generating the necessary torque for beam rotation. This mechatronic balancing mechanism falls into the category of unstable, under-actuated, multivariable systems with highly nonlinear dynamics. The final section of the article presents the design and control of a single-joint manipulator arm with pneumatic muscle actuators that enable some new features for the controlled systems. Fluid Power 2015 176
Diabetes management in the primary care setting: a comparison of physicians' performance by gender.
(2018)

BACKGROUND
A major shift in the gender of the medical-doctor workforce is now underway, and all over the world it is expected that an average 65% of the medical workforce will be women by 2030. In addition, an aging population means that chronic diseases, such as diabetes, are becoming more prevalent and the demand for care is rising. There is growing evidence of female physicians performing better than male physicians.AimOur study aimed to investigate whether any differences in diabetes process indicators are associated with gender, and/or the interaction between gender and different organizational models.Design and settingA population-based cross-sectional analysis was conducted on a large data set obtained by processing the public health administration databases of seven Italian local health units (LHUs). The seven LHUs, distributed all over the Italian peninsula in seven different regions, took part in a national project called MEDINA, with the focus on chronic disease management in primary care (PC).


METHODS
A total score was calculated for the average performance in the previously listed five indicators, representing global adherence to a quality management of patients with diabetes. A multilevel analysis was applied to see how LHUs affected the outcome. A quantile regression model was also fitted.


RESULTS
Our study included 2287 Italian general practitioners (586 of them female) caring for a total of 2 646 059 patients. Analyzing the performance scores confirmed that female general practitioners obtained better results than males. The differences between males and females were stronger on the 25th and 75th percentiles of the score than on the median values. The interaction between gender and LHU was not significant.


CONCLUSION
Our study evidenced that female physicians perform better than males in providing PC for diabetes independently by the different organizational models. Further research to understand the reasons for these gender differences is needed.
Direct type-specific conic fitting and eigenvalue bias correction
(2008)

A new method to fit specific types of conics to scattered data points is introduced. Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure. Fitting of parabolas is achieved by determining an orthogonal basis vector set in the Grassmannian space of the quadratic terms’ coefficients. The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers. This is the first known direct solution for parabola specific fitting. Furthermore, the inherent bias of a linear conic fit is addressed. We propose a linear method of correcting this bias, producing better geometric fits which are still constrained to specific conic type. 2007 Published by Elsevier B.V.
Articulatory feature-based pronunciation modeling
(2016)

Spoken language, especially conversational speech, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on conversational speech, and it has been very difficult to mitigate in traditional phonebased approaches to speech recognition. An alternative approach, which has been studied by ourselves and others, is one based on sub-phonetic features rather than phones. In such an approach, a word’s pronunciation is represented as multiple streams of phonological features rather than a single stream of phones. Features may correspond to the positions of the speech articulators, such as the lips and tongue, or may be more abstract categories such as manner and place. This article reviews our work on a particular type of articulatory feature-based pronunciation model. The model allows for asynchrony between features, as well as per-feature substitutions, making it more natural to account for many pronunciation changes that are difficult to handle with phone-based models. Such models can be efficiently represented as dynamic Bayesian networks. The feature-based models improve significantly over phone-based counterparts in terms of frame perplexity and lexical access accuracy. The remainder of the article discusses related work and future directions.
Attacking an AES-Enabled NFC Tag: Implications from Design to a Real-World Scenario
(2012)

Radio-frequency identification (RFID) technology is the enabler for applications like the future internet of things (IoT), where security plays an important role. When integrating security to RFID tags, not only the cryptographic algorithms need to be secure but also their implementation. In this work we present differential power analysis (DPA) and differential electromagnetic analysis (DEMA) attacks on a securityenabled RFID tag. The attacks are conducted on both an ASIC-chip version and on an FPGA-prototype version of the tag. The design of the ASIC version equals that of commercial RFID tags and has analog and digital part integrated on a single chip. Target of the attacks is an implementation of the Advanced Encryption Standard (AES) with 128-bit key length and DPA countermeasures. The countermeasures are shuffling of operations and insertion of dummy rounds. Our results illustrate that the effort for successfully attacking the ASIC chip in a real-world scenario is only 4.5 times higher than for the FPGA prototype in a laboratory environment. This let us come to the conclusion that the effort for attacking contactless devices like RFID tags is only slightly higher than that for contact-based devices. The results further underline that the design of countermeasures like the insertion of dummy rounds has to be done with great care, since the detection of patterns in power or electromagnetic traces can be used to significantly lower the attacking effort.
Building Program Vector Representations for Deep Learning
(2015)

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the “coding criterion” to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than “shallow” methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.
A case against convexity in conceptual spaces
(2016)

The notion of conceptual space, proposed by Gärdenfors as a framework for the representation of concepts and knowledge, has been highly influential over the last decade or so. One of the main theses involved in this approach is that the conceptual regions associated with properties, concepts, verbs, etc. are convex. The aim of this paper is to show that such a constraint—that of the convexity of the geometry of conceptual regions—is problematic; both from a theoretical perspective and with regard to the inner workings of the theory itself. On the one hand, all the arguments provided in favor of the convexity of conceptual regions rest on controversial assumptions. Additionally, his argument in support of a Euclidean metric, based on the integral character of conceptual dimensions, is weak, and under non-Euclidean metrics the structure of regions may be non-convex. Furthermore, even if the metric were Euclidean, the convexity constraint could be not satisfied if concepts were differentially weighted. On the other hand, Gärdenfors’ convexity constraint is brought into question by the own inner workings of conceptual spaces because: (i) some of the allegedly convex properties of concepts are not convex; (ii) the conceptual regions resulting from the combination of convex properties can be non-convex; (iii) convex regions may co-vary in non-convex ways; and (iv) his definition of verbs is incompatible with a definition of properties in terms of convex regions. Therefore, the mandatory character of the convexity requirement for regions in a conceptual space theory should be reconsidered.
Inductive Design of Maturity Models: Applying the Rasch Algorithm for Design Science Research
(2011)

Maturity models are an established means to systematically document and guide the development of organizations using archetypal capability levels. Often, these models lack a sound foundation and/or are derived on the basis of an arbitrary design method. In order to foster the design of relevant and rigorous artifacts, this paper presents a method for maturity model construction that applies the Rasch algorithm and cluster analysis as a sound methodical foundation. The Rasch algorithm is widely used to improve scholarly intelligence and attainment tests. In order to demonstrate the application of the proposed method and to evaluate its usability and applicability, we present a design exemplar in the business intelligence domain.
Increased prefrontal and parietal activity after training of working memory
(2004)

Working memory capacity has traditionally been thought to be constant. Recent studies, however, suggest that working memory can be improved by training. In this study, we have investigated the changes in brain activity that are induced by working memory training. Two experiments were carried out in which healthy, adult human subjects practiced working memory tasks for 5 weeks. Brain activity was measured with functional magnetic resonance imaging (fMRI) before, during and after training. After training, brain activity that was related to working memory increased in the middle frontal gyrus and superior and inferior parietal cortices. The changes in cortical activity could be evidence of training-induced plasticity in the neural systems that underlie working memory.
Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach
(2018)

Aerial images are subject to various types of noise, which restricts the recognition and analysis of images, target monitoring, and search services. At present, deep learning is successful in image recognition. However, traditional convolutional neural networks (CNNs) extract the main features of an image to predict directly and are limited by the requirements of the training sample size (i.e., a small data size is not successful enough). In this paper, using a small sample size, we propose an aerial-image denoising recognition model based on CNNs with a multi-scale residual learning approach. The proposed model has the following three advantages: (1) Instead of directly learning latent clean images, the proposed model learns the noise from noisy images and then subtracts the learned residual from the noisy images to obtain reconstructed (denoised) images; (2) The developed image denoising recognition model is beneficial to small training datasets; (3) We use multi-scale residual learning as a learning approach, and dropout is introduced into the model architecture to force the network to learn to generalize well enough. Our experimental results on aerial-image denoising recognition reveal that the proposed approach is highly superior to the other state-of-the-art methods.
RECONCEPTUALISING GAMIFICATION : PLAY AND PEDAGOGY
(2014)

Gamification is a complex and controversial concept. It has been both embraced as a marketing and education revolution, and dismissed as practice of exploitation. Contested within the debate around gamification has been the very concept of what a game is, what the core mechanics of games are, and whether gamification truly mobilises these core mechanics. This paper will challenge the foundation of this debate through reconceptualising gamification not as a simple set of techniques and mechanics, but as a pedagogic heritage, an alternative framework for training and shaping participant behaviour that has at its core the concepts of entertainment and engagement. In doing so it will recontextualise current practices of gamification into a longer and deeper history, and suggest potential pathways for more sophisticated gamification in the future.
Minimum Near-Convex Shape Decomposition.
(2013)

Shape decomposition is a fundamental problem for part-based shape representation. We propose the Minimum Near-Convex Decomposition (MNCD) to decompose arbitrary shapes into minimum number of &#x0022;near-convex&#x0022; parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of non-intersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via Binary Integer Linear Programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts, and thus leads to robust shape representation.
A 3-kW Wireless Power Transfer System for Sightseeing Car Supercapacitor Charge
(2017)

Wireless power transfer (WPT) is the preferred charging method for electric vehicles (EVs) powered by battery and supercapacitor. In this paper, a novel WPT system with constant current charging capability for sightseeing car with supercapacitor storage is designed. First, an optimized magnetic coupler using ferrite cores and magnetic shielding structure is proposed to ensure stable power transfer and high efficiency. Compared with the traditional planar shape ferrite core coupler, the proposed magnetic coupler requires lesser ferrite material without degrading the performance of the WPT system. Second, the model of supercapacitor is applied to the WPT system and the relationship between equivalent load resistances of supercapacitor and charging time is analyzed in detail. Then, a Buck converter with proportional integral (PI) controller is implemented on the secondary side to maintain constant charging current for the variable load. Finally, the proposed design is verified by experiments. Constant charging current of 31.5 A across transfer distance of 15 cm is achieved. The peak transfer power and system efficiency are 2.86 kW and 88.05%, respectively.
Step Climbing Omnidirectional Mobile Robot with Passive Linkages
(2005)

In this paper, we develop a holonomic omni-directional mobile vehicle with step-climbing ability. This system realizes omni-directional motion on flat floor using special wheels and passes over the step in forward or backward direction using the passive linkage mechanism. Our key ideas are the following topics. First topic is new omnidirectional mobile mechanism which consists of special wheels and passive linkage mechanism. Second topic is new passive linkage mechanism which can change its body configuration easily when the vehicle passes over the step. Third topic is wheel control reference derivation based on the body configuration, which changes passively during step climbing for reducing wheel slippage. Last topic is wheel control method which keeps the rotation velocity coordination among the wheels for reducing wheel slippage and increasing the step-climbing performance. We verified the effectiveness of our proposed method by the computer simulations and experiments. Utilizing our proposed mechanism and control systems, the vehicle has both omnidirectional mobility and step overcoming function. Furthermore, our developing vehicle can pass over the 128[mm] height step using the wheel which radius is 66[mm]. Its performance is three times larger than one of general wheeled vehicle.
Pay Attention to the Ending: Strong Neural Baselines for the ROC Story Cloze Task
(2017)

We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings. We develop a model that uses hierarchical recurrent networks with attention to encode the sentences in the story and score candidate endings. By discarding the large training set and only training on the validation set, we achieve an accuracy of 74.7%. Even when we discard the story plots (sentences before the ending) and only train to choose the better of two endings, we can still reach 72.5%. We then analyze this “ending-only” task setting. We estimate human accuracy to be 78% and find several types of clues that lead to this high accuracy, including those related to sentiment, negation, and general ending likelihood regardless of the story context.